| from typing import List |
|
|
| from inference.core.entities.responses.inference import Keypoint |
| from inference.core.exceptions import ModelArtefactError |
|
|
|
|
| def superset_keypoints_count(keypoints_metadata={}) -> int: |
| """Returns the number of keypoints in the superset.""" |
| max_keypoints = 0 |
| for keypoints in keypoints_metadata.values(): |
| if len(keypoints) > max_keypoints: |
| max_keypoints = len(keypoints) |
| return max_keypoints |
|
|
|
|
| def model_keypoints_to_response( |
| keypoints_metadata: dict, |
| keypoints: List[float], |
| predicted_object_class_id: int, |
| keypoint_confidence_threshold: float, |
| ) -> List[Keypoint]: |
| if keypoints_metadata is None: |
| raise ModelArtefactError("Keypoints metadata not available.") |
| keypoint_id2name = keypoints_metadata[predicted_object_class_id] |
| results = [] |
| for keypoint_id in range(len(keypoints) // 3): |
| if keypoint_id >= len(keypoint_id2name): |
| |
| break |
| confidence = keypoints[3 * keypoint_id + 2] |
| if confidence < keypoint_confidence_threshold: |
| continue |
| keypoint = Keypoint( |
| x=keypoints[3 * keypoint_id], |
| y=keypoints[3 * keypoint_id + 1], |
| confidence=confidence, |
| class_id=keypoint_id, |
| class_name=keypoint_id2name[keypoint_id], |
| ) |
| results.append(keypoint) |
| return results |
|
|
